Federated machine learning (FedML) [1]}, [2]} has been proposed as a solution to the input data privacy problem in machine learning. In FedML, multiple parties, or clients, jointly train a model while keeping data local and private. Instead of moving data to a central storage system for model training,
computation is brought to data at local sites and incremental model updates are computed and combined into a global model. In this way, only model parameters are shared.
The majority of work to date has dealt with artificial neural networks and deep learning [1]}, [4]}, [5]}, [6]} but there is also research done on federated versions of other statistical learning methods such as random forests [7]}.
